Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory195.8 KiB
Average record size in memory401.0 B

Variable types

Text2
Categorical5
Numeric7

Alerts

Store has constant value "1" Constant
Brand is highly overall correlated with Classification and 1 other fieldsHigh correlation
Classification is highly overall correlated with Brand and 3 other fieldsHigh correlation
ExciseTax is highly overall correlated with SalesDollars and 1 other fieldsHigh correlation
SalesDollars is highly overall correlated with ExciseTax and 2 other fieldsHigh correlation
SalesPrice is highly overall correlated with SalesDollarsHigh correlation
SalesQuantity is highly overall correlated with ExciseTax and 1 other fieldsHigh correlation
Size is highly overall correlated with Classification and 2 other fieldsHigh correlation
VendorName is highly overall correlated with Brand and 4 other fieldsHigh correlation
VendorNo is highly overall correlated with VendorNameHigh correlation
Volume is highly overall correlated with Classification and 2 other fieldsHigh correlation
Size is highly imbalanced (64.4%) Imbalance

Reproduction

Analysis started2025-09-25 08:06:47.617834
Analysis finished2025-09-25 08:07:00.120486
Duration12.5 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct95
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size33.7 KiB
2025-09-25T14:07:00.603672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.756
Min length18

Characters and Unicode

Total characters9878
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)4.4%

Sample

1st row1_HARDERSFIELD_1004
2nd row1_HARDERSFIELD_1004
3rd row1_HARDERSFIELD_1004
4th row1_HARDERSFIELD_1004
5th row1_HARDERSFIELD_1005
ValueCountFrequency (%)
1_hardersfield_11219 22
 
4.4%
1_hardersfield_1180 17
 
3.4%
1_hardersfield_10727 15
 
3.0%
1_hardersfield_11825 14
 
2.8%
1_hardersfield_11826 14
 
2.8%
1_hardersfield_11141 13
 
2.6%
1_hardersfield_10062 13
 
2.6%
1_hardersfield_11698 12
 
2.4%
1_hardersfield_10626 12
 
2.4%
1_hardersfield_11828 12
 
2.4%
Other values (85) 356
71.2%
2025-09-25T14:07:01.083901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1359
13.8%
_ 1000
10.1%
D 1000
10.1%
R 1000
10.1%
E 1000
10.1%
H 500
 
5.1%
A 500
 
5.1%
S 500
 
5.1%
F 500
 
5.1%
I 500
 
5.1%
Other values (10) 2019
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1359
13.8%
_ 1000
10.1%
D 1000
10.1%
R 1000
10.1%
E 1000
10.1%
H 500
 
5.1%
A 500
 
5.1%
S 500
 
5.1%
F 500
 
5.1%
I 500
 
5.1%
Other values (10) 2019
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1359
13.8%
_ 1000
10.1%
D 1000
10.1%
R 1000
10.1%
E 1000
10.1%
H 500
 
5.1%
A 500
 
5.1%
S 500
 
5.1%
F 500
 
5.1%
I 500
 
5.1%
Other values (10) 2019
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1359
13.8%
_ 1000
10.1%
D 1000
10.1%
R 1000
10.1%
E 1000
10.1%
H 500
 
5.1%
A 500
 
5.1%
S 500
 
5.1%
F 500
 
5.1%
I 500
 
5.1%
Other values (10) 2019
20.4%

Store
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2025-09-25T14:07:01.226622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T14:07:01.353792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

Most occurring characters

ValueCountFrequency (%)
1 500
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 500
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 500
100.0%

Brand
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8709.88
Minimum115
Maximum11888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:01.503601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile1019.95
Q110062
median10603
Q311219
95-th percentile11826
Maximum11888
Range11773
Interquartile range (IQR)1157

Descriptive statistics

Standard deviation4170.3728
Coefficient of variation (CV)0.47880945
Kurtosis-0.27141052
Mean8709.88
Median Absolute Deviation (MAD)613.5
Skewness-1.2816317
Sum4354940
Variance17392010
MonotonicityNot monotonic
2025-09-25T14:07:01.702948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11219 22
 
4.4%
1180 17
 
3.4%
10727 15
 
3.0%
11825 14
 
2.8%
11826 14
 
2.8%
11141 13
 
2.6%
10062 13
 
2.6%
11698 12
 
2.4%
10626 12
 
2.4%
11828 12
 
2.4%
Other values (85) 356
71.2%
ValueCountFrequency (%)
115 10
2.0%
1004 4
 
0.8%
1005 4
 
0.8%
1006 1
 
0.2%
1009 1
 
0.2%
1012 1
 
0.2%
1013 2
 
0.4%
1019 2
 
0.4%
1020 1
 
0.2%
1021 1
 
0.2%
ValueCountFrequency (%)
11888 5
 
1.0%
11828 12
2.4%
11827 7
1.4%
11826 14
2.8%
11825 14
2.8%
11771 1
 
0.2%
11698 12
2.4%
11488 4
 
0.8%
11480 1
 
0.2%
11456 2
 
0.4%
Distinct93
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2025-09-25T14:07:02.240039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length25
Mean length22.822
Min length10

Characters and Unicode

Total characters11411
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)4.4%

Sample

1st rowJim Beam w/2 Rocks Glasses
2nd rowJim Beam w/2 Rocks Glasses
3rd rowJim Beam w/2 Rocks Glasses
4th rowJim Beam w/2 Rocks Glasses
5th rowMaker's Mark Combo Pack
ValueCountFrequency (%)
svgn 138
 
6.8%
cab 78
 
3.9%
bl 60
 
3.0%
pnt 51
 
2.5%
cupcake 47
 
2.3%
nr 45
 
2.2%
chard 41
 
2.0%
cal 35
 
1.7%
b 30
 
1.5%
williams 29
 
1.4%
Other values (244) 1463
72.5%
2025-09-25T14:07:02.935771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1517
 
13.3%
a 1051
 
9.2%
e 736
 
6.4%
o 694
 
6.1%
l 659
 
5.8%
r 623
 
5.5%
n 620
 
5.4%
i 456
 
4.0%
s 394
 
3.5%
C 391
 
3.4%
Other values (53) 4270
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1517
 
13.3%
a 1051
 
9.2%
e 736
 
6.4%
o 694
 
6.1%
l 659
 
5.8%
r 623
 
5.5%
n 620
 
5.4%
i 456
 
4.0%
s 394
 
3.5%
C 391
 
3.4%
Other values (53) 4270
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1517
 
13.3%
a 1051
 
9.2%
e 736
 
6.4%
o 694
 
6.1%
l 659
 
5.8%
r 623
 
5.5%
n 620
 
5.4%
i 456
 
4.0%
s 394
 
3.5%
C 391
 
3.4%
Other values (53) 4270
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1517
 
13.3%
a 1051
 
9.2%
e 736
 
6.4%
o 694
 
6.1%
l 659
 
5.8%
r 623
 
5.5%
n 620
 
5.4%
i 456
 
4.0%
s 394
 
3.5%
C 391
 
3.4%
Other values (53) 4270
37.4%

Size
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
750mL
391 
1.5L
45 
1.75L
 
21
Liter
 
16
5L
 
9
Other values (8)
 
18

Length

Max length10
Median length5
Mean length5.03
Min length2

Characters and Unicode

Total characters2515
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row750mL
2nd row750mL
3rd row750mL
4th row750mL
5th row375mL 2 Pk

Common Values

ValueCountFrequency (%)
750mL 391
78.2%
1.5L 45
 
9.0%
1.75L 21
 
4.2%
Liter 16
 
3.2%
5L 9
 
1.8%
200mL 3 Pk 4
 
0.8%
375mL 2 Pk 4
 
0.8%
750mL + 3/ 3
 
0.6%
50mL 3 Pk 2
 
0.4%
100mL 4 Pk 2
 
0.4%
Other values (3) 3
 
0.6%

Length

2025-09-25T14:07:03.069350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
750ml 395
73.7%
1.5l 45
 
8.4%
1.75l 21
 
3.9%
liter 16
 
3.0%
pk 15
 
2.8%
3 11
 
2.1%
5l 9
 
1.7%
375ml 5
 
0.9%
200ml 4
 
0.7%
2 4
 
0.7%
Other values (4) 11
 
2.1%

Most occurring characters

ValueCountFrequency (%)
L 500
19.9%
5 478
19.0%
7 421
16.7%
0 410
16.3%
m 409
16.3%
1 68
 
2.7%
. 66
 
2.6%
36
 
1.4%
i 16
 
0.6%
e 16
 
0.6%
Other values (9) 95
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 500
19.9%
5 478
19.0%
7 421
16.7%
0 410
16.3%
m 409
16.3%
1 68
 
2.7%
. 66
 
2.6%
36
 
1.4%
i 16
 
0.6%
e 16
 
0.6%
Other values (9) 95
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 500
19.9%
5 478
19.0%
7 421
16.7%
0 410
16.3%
m 409
16.3%
1 68
 
2.7%
. 66
 
2.6%
36
 
1.4%
i 16
 
0.6%
e 16
 
0.6%
Other values (9) 95
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 500
19.9%
5 478
19.0%
7 421
16.7%
0 410
16.3%
m 409
16.3%
1 68
 
2.7%
. 66
 
2.6%
36
 
1.4%
i 16
 
0.6%
e 16
 
0.6%
Other values (9) 95
 
3.8%

SalesQuantity
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.064
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:03.202642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.5606675
Coefficient of variation (CV)1.2406335
Kurtosis66.483286
Mean2.064
Median Absolute Deviation (MAD)0
Skewness6.431217
Sum1032
Variance6.557018
MonotonicityNot monotonic
2025-09-25T14:07:03.319391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 303
60.6%
2 107
 
21.4%
3 28
 
5.6%
4 23
 
4.6%
6 12
 
2.4%
5 7
 
1.4%
12 6
 
1.2%
7 4
 
0.8%
9 3
 
0.6%
8 2
 
0.4%
Other values (4) 5
 
1.0%
ValueCountFrequency (%)
1 303
60.6%
2 107
 
21.4%
3 28
 
5.6%
4 23
 
4.6%
5 7
 
1.4%
6 12
 
2.4%
7 4
 
0.8%
8 2
 
0.4%
9 3
 
0.6%
10 1
 
0.2%
ValueCountFrequency (%)
36 1
 
0.2%
15 1
 
0.2%
14 2
 
0.4%
12 6
1.2%
10 1
 
0.2%
9 3
 
0.6%
8 2
 
0.4%
7 4
 
0.8%
6 12
2.4%
5 7
1.4%

SalesDollars
Real number (ℝ)

High correlation 

Distinct115
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.56852
Minimum3.99
Maximum1007.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:03.485351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.99
5-th percentile8.49
Q111.365
median19.98
Q333.96
95-th percentile85.98
Maximum1007.64
Range1003.65
Interquartile range (IQR)22.595

Descriptive statistics

Standard deviation51.625674
Coefficient of variation (CV)1.6888509
Kurtosis258.23915
Mean30.56852
Median Absolute Deviation (MAD)9.99
Skewness14.027955
Sum15284.26
Variance2665.2102
MonotonicityNot monotonic
2025-09-25T14:07:03.685796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.99 40
 
8.0%
10.99 35
 
7.0%
8.99 16
 
3.2%
16.99 16
 
3.2%
19.98 15
 
3.0%
8.49 15
 
3.0%
11.99 14
 
2.8%
14.99 14
 
2.8%
20.99 13
 
2.6%
27.99 13
 
2.6%
Other values (105) 309
61.8%
ValueCountFrequency (%)
3.99 1
 
0.2%
4.99 1
 
0.2%
6.29 4
 
0.8%
6.99 4
 
0.8%
7.49 2
 
0.4%
7.95 2
 
0.4%
8.49 15
 
3.0%
8.99 16
 
3.2%
9.95 5
 
1.0%
9.99 40
8.0%
ValueCountFrequency (%)
1007.64 1
 
0.2%
224.85 1
 
0.2%
179.94 1
 
0.2%
167.88 1
 
0.2%
153.86 1
 
0.2%
139.9 1
 
0.2%
134.91 2
0.4%
119.88 3
0.6%
118.86 1
 
0.2%
113.94 1
 
0.2%

SalesPrice
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.966
Minimum3.99
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:03.875596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.99
5-th percentile8.49
Q19.99
median11.99
Q317.99
95-th percentile34.99
Maximum99.99
Range96
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.109741
Coefficient of variation (CV)0.69583748
Kurtosis19.126269
Mean15.966
Median Absolute Deviation (MAD)3
Skewness3.6839041
Sum7983
Variance123.42635
MonotonicityNot monotonic
2025-09-25T14:07:04.061482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9.99 77
15.4%
10.99 54
 
10.8%
8.49 44
 
8.8%
14.99 36
 
7.2%
15.99 24
 
4.8%
11.99 20
 
4.0%
16.99 19
 
3.8%
8.99 19
 
3.8%
27.99 18
 
3.6%
12.99 17
 
3.4%
Other values (33) 172
34.4%
ValueCountFrequency (%)
3.99 1
 
0.2%
4.99 2
 
0.4%
6.29 8
 
1.6%
6.99 7
 
1.4%
7.49 3
 
0.6%
7.95 2
 
0.4%
8.49 44
8.8%
8.99 19
 
3.8%
9.95 6
 
1.2%
9.99 77
15.4%
ValueCountFrequency (%)
99.99 2
0.4%
74.99 4
0.8%
62.99 1
 
0.2%
58.99 1
 
0.2%
49.99 1
 
0.2%
46.99 1
 
0.2%
44.99 2
0.4%
42.99 4
0.8%
39.99 4
0.8%
35.99 1
 
0.2%

SalesDate
Categorical

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2024-01-23
 
30
2024-01-09
 
29
2024-01-15
 
28
2024-01-29
 
28
2024-01-30
 
24
Other values (26)
361 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-01
2nd row2024-01-02
3rd row2024-01-03
4th row2024-01-08
5th row2024-01-09

Common Values

ValueCountFrequency (%)
2024-01-23 30
 
6.0%
2024-01-09 29
 
5.8%
2024-01-15 28
 
5.6%
2024-01-29 28
 
5.6%
2024-01-30 24
 
4.8%
2024-01-22 23
 
4.6%
2024-01-02 23
 
4.6%
2024-01-06 21
 
4.2%
2024-01-07 20
 
4.0%
2024-01-20 19
 
3.8%
Other values (21) 255
51.0%

Length

2025-09-25T14:07:04.218412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-01-23 30
 
6.0%
2024-01-09 29
 
5.8%
2024-01-15 28
 
5.6%
2024-01-29 28
 
5.6%
2024-01-30 24
 
4.8%
2024-01-22 23
 
4.6%
2024-01-02 23
 
4.6%
2024-01-06 21
 
4.2%
2024-01-07 20
 
4.0%
2024-01-20 19
 
3.8%
Other values (21) 255
51.0%

Most occurring characters

ValueCountFrequency (%)
2 1234
24.7%
0 1216
24.3%
- 1000
20.0%
1 667
13.3%
4 532
10.6%
3 96
 
1.9%
9 64
 
1.3%
6 51
 
1.0%
5 50
 
1.0%
7 49
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1234
24.7%
0 1216
24.3%
- 1000
20.0%
1 667
13.3%
4 532
10.6%
3 96
 
1.9%
9 64
 
1.3%
6 51
 
1.0%
5 50
 
1.0%
7 49
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1234
24.7%
0 1216
24.3%
- 1000
20.0%
1 667
13.3%
4 532
10.6%
3 96
 
1.9%
9 64
 
1.3%
6 51
 
1.0%
5 50
 
1.0%
7 49
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1234
24.7%
0 1216
24.3%
- 1000
20.0%
1 667
13.3%
4 532
10.6%
3 96
 
1.9%
9 64
 
1.3%
6 51
 
1.0%
5 50
 
1.0%
7 49
 
1.0%

Volume
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.05
Minimum50
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:04.534917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile750
Q1750
median750
Q3750
95-th percentile1750
Maximum5000
Range4950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation630.03272
Coefficient of variation (CV)0.67814727
Kurtosis29.04688
Mean929.05
Median Absolute Deviation (MAD)0
Skewness4.9829727
Sum464525
Variance396941.23
MonotonicityNot monotonic
2025-09-25T14:07:04.651523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
750 395
79.0%
1500 45
 
9.0%
1750 21
 
4.2%
1000 16
 
3.2%
5000 9
 
1.8%
375 5
 
1.0%
200 4
 
0.8%
50 3
 
0.6%
100 2
 
0.4%
ValueCountFrequency (%)
50 3
 
0.6%
100 2
 
0.4%
200 4
 
0.8%
375 5
 
1.0%
750 395
79.0%
1000 16
 
3.2%
1500 45
 
9.0%
1750 21
 
4.2%
5000 9
 
1.8%
ValueCountFrequency (%)
5000 9
 
1.8%
1750 21
 
4.2%
1500 45
 
9.0%
1000 16
 
3.2%
750 395
79.0%
375 5
 
1.0%
200 4
 
0.8%
100 2
 
0.4%
50 3
 
0.6%

Classification
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
2
388 
1
112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

Length

2025-09-25T14:07:04.785110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T14:07:04.891694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

Most occurring characters

ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 388
77.6%
1 112
 
22.4%

ExciseTax
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6151
Minimum0.05
Maximum28.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:05.000366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.11
Q10.11
median0.22
Q30.75
95-th percentile1.84
Maximum28.35
Range28.3
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation1.6167603
Coefficient of variation (CV)2.6284512
Kurtosis180.15935
Mean0.6151
Median Absolute Deviation (MAD)0.11
Skewness11.635322
Sum307.55
Variance2.613914
MonotonicityNot monotonic
2025-09-25T14:07:05.156025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.11 184
36.8%
0.22 102
20.4%
0.79 56
 
11.2%
0.45 29
 
5.8%
0.34 25
 
5.0%
0.68 13
 
2.6%
1.05 13
 
2.6%
1.57 9
 
1.8%
3.67 8
 
1.6%
1.84 8
 
1.6%
Other values (21) 53
 
10.6%
ValueCountFrequency (%)
0.05 3
 
0.6%
0.1 2
 
0.4%
0.11 184
36.8%
0.21 3
 
0.6%
0.22 102
20.4%
0.34 25
 
5.0%
0.39 4
 
0.8%
0.42 1
 
0.2%
0.45 29
 
5.8%
0.56 5
 
1.0%
ValueCountFrequency (%)
28.35 1
 
0.2%
11.02 1
 
0.2%
9.45 1
 
0.2%
7.35 3
 
0.6%
5.51 1
 
0.2%
3.67 8
1.6%
3.15 1
 
0.2%
2.1 3
 
0.6%
2.02 1
 
0.2%
1.84 8
1.6%

VendorNo
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7177.68
Minimum480
Maximum90046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-25T14:07:05.318585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile1392
Q13252
median7153
Q39744
95-th percentile12546
Maximum90046
Range89566
Interquartile range (IQR)6492

Descriptive statistics

Standard deviation8294.0517
Coefficient of variation (CV)1.1555338
Kurtosis77.859093
Mean7177.68
Median Absolute Deviation (MAD)2728
Skewness8.0067093
Sum3588840
Variance68791294
MonotonicityNot monotonic
2025-09-25T14:07:05.451120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4425 76
15.2%
3252 58
11.6%
9815 52
10.4%
2000 33
 
6.6%
9552 32
 
6.4%
3924 32
 
6.4%
7153 29
 
5.8%
10754 24
 
4.8%
1392 21
 
4.2%
9165 21
 
4.2%
Other values (16) 122
24.4%
ValueCountFrequency (%)
480 1
 
0.2%
516 6
 
1.2%
1128 10
 
2.0%
1392 21
 
4.2%
1590 3
 
0.6%
2000 33
6.6%
2555 2
 
0.4%
3252 58
11.6%
3664 2
 
0.4%
3924 32
6.4%
ValueCountFrequency (%)
90046 1
 
0.2%
90024 3
 
0.6%
17035 10
 
2.0%
12546 18
 
3.6%
10754 24
4.8%
9819 5
 
1.0%
9815 52
10.4%
9744 18
 
3.6%
9552 32
6.4%
9165 21
4.2%

VendorName
Categorical

High correlation 

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size36.8 KiB
MARTIGNETTI COMPANIES
76 
E & J GALLO WINERY
58 
WINE GROUP INC
52 
SOUTHERN WINE & SPIRITS NE
33 
M S WALKER INC
32 
Other values (21)
249 

Length

Max length27
Median length27
Mean length26.088
Min length21

Characters and Unicode

Total characters13044
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowJIM BEAM BRANDS COMPANY
2nd rowJIM BEAM BRANDS COMPANY
3rd rowJIM BEAM BRANDS COMPANY
4th rowJIM BEAM BRANDS COMPANY
5th rowJIM BEAM BRANDS COMPANY

Common Values

ValueCountFrequency (%)
MARTIGNETTI COMPANIES 76
15.2%
E & J GALLO WINERY 58
11.6%
WINE GROUP INC 52
10.4%
SOUTHERN WINE & SPIRITS NE 33
 
6.6%
M S WALKER INC 32
 
6.4%
HEAVEN HILL DISTILLERIES 32
 
6.4%
PINE STATE TRADING CO 29
 
5.8%
PERFECTA WINES 24
 
4.8%
CONSTELLATION BRANDS INC 21
 
4.2%
ULTRA BEVERAGE COMPANY LLP 21
 
4.2%
Other values (16) 122
24.4%

Length

2025-09-25T14:07:05.600998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 144
 
8.5%
111
 
6.6%
wine 90
 
5.3%
companies 76
 
4.5%
martignetti 76
 
4.5%
e 58
 
3.4%
j 58
 
3.4%
winery 58
 
3.4%
gallo 58
 
3.4%
group 52
 
3.1%
Other values (53) 907
53.7%

Most occurring characters

ValueCountFrequency (%)
4441
34.0%
E 958
 
7.3%
I 938
 
7.2%
N 876
 
6.7%
A 644
 
4.9%
R 596
 
4.6%
T 574
 
4.4%
S 530
 
4.1%
O 456
 
3.5%
L 438
 
3.4%
Other values (18) 2593
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4441
34.0%
E 958
 
7.3%
I 938
 
7.2%
N 876
 
6.7%
A 644
 
4.9%
R 596
 
4.6%
T 574
 
4.4%
S 530
 
4.1%
O 456
 
3.5%
L 438
 
3.4%
Other values (18) 2593
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4441
34.0%
E 958
 
7.3%
I 938
 
7.2%
N 876
 
6.7%
A 644
 
4.9%
R 596
 
4.6%
T 574
 
4.4%
S 530
 
4.1%
O 456
 
3.5%
L 438
 
3.4%
Other values (18) 2593
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4441
34.0%
E 958
 
7.3%
I 938
 
7.2%
N 876
 
6.7%
A 644
 
4.9%
R 596
 
4.6%
T 574
 
4.4%
S 530
 
4.1%
O 456
 
3.5%
L 438
 
3.4%
Other values (18) 2593
19.9%

Interactions

2025-09-25T14:06:58.367684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:49.448902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:50.881186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:52.322074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:53.638134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.833732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.260221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:58.521907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:49.718928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:51.118223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:52.534859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:53.828493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.988976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.437672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:58.688020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:49.928395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:51.324732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:52.739377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.045759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:56.453943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.605100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:58.837882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:50.119291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:51.522603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:52.926481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.211653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:56.605697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.768821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:59.004453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:50.262986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:51.742081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:53.106742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.352579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:56.783100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.921642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:59.180228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:50.438942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:51.959598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:53.284766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.507336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:56.922291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:58.080800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:59.355982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:50.701600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:52.138389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:53.474416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:54.668894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:57.105438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:06:58.221943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-25T14:07:05.734199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BrandClassificationExciseTaxSalesDateSalesDollarsSalesPriceSalesQuantitySizeVendorNameVendorNoVolume
Brand1.0000.999-0.3840.000-0.144-0.4890.1990.4470.7570.046-0.061
Classification0.9991.0000.3270.0000.0510.4710.1150.6450.9700.0580.522
ExciseTax-0.3840.3271.0000.0000.6080.2620.5100.3340.193-0.0420.423
SalesDate0.0000.0000.0001.0000.0000.0000.0430.0000.0000.0000.000
SalesDollars-0.1440.0510.6080.0001.0000.5500.6970.0000.000-0.1030.014
SalesPrice-0.4890.4710.2620.0000.5501.000-0.1440.4290.429-0.1850.066
SalesQuantity0.1990.1150.5100.0430.697-0.1441.0000.0000.068-0.0160.022
Size0.4470.6450.3340.0000.0000.4290.0001.0000.5090.2210.992
VendorName0.7570.9700.1930.0000.0000.4290.0680.5091.0000.9770.521
VendorNo0.0460.058-0.0420.000-0.103-0.185-0.0160.2210.9771.000-0.283
Volume-0.0610.5220.4230.0000.0140.0660.0220.9920.521-0.2831.000

Missing values

2025-09-25T14:06:59.587391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-25T14:06:59.842673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

InventoryIdStoreBrandDescriptionSizeSalesQuantitySalesDollarsSalesPriceSalesDateVolumeClassificationExciseTaxVendorNoVendorName
01_HARDERSFIELD_100411004Jim Beam w/2 Rocks Glasses750mL116.4916.492024-01-01750.010.7912546JIM BEAM BRANDS COMPANY
11_HARDERSFIELD_100411004Jim Beam w/2 Rocks Glasses750mL232.9816.492024-01-02750.011.5712546JIM BEAM BRANDS COMPANY
21_HARDERSFIELD_100411004Jim Beam w/2 Rocks Glasses750mL116.4916.492024-01-03750.010.7912546JIM BEAM BRANDS COMPANY
31_HARDERSFIELD_100411004Jim Beam w/2 Rocks Glasses750mL114.4914.492024-01-08750.010.7912546JIM BEAM BRANDS COMPANY
41_HARDERSFIELD_100511005Maker's Mark Combo Pack375mL 2 Pk269.9834.992024-01-09375.010.7912546JIM BEAM BRANDS COMPANY
51_HARDERSFIELD_100511005Maker's Mark Combo Pack375mL 2 Pk134.9934.992024-01-15375.010.3912546JIM BEAM BRANDS COMPANY
61_HARDERSFIELD_100511005Maker's Mark Combo Pack375mL 2 Pk134.9934.992024-01-22375.010.3912546JIM BEAM BRANDS COMPANY
71_HARDERSFIELD_100511005Maker's Mark Combo Pack375mL 2 Pk134.9934.992024-01-30375.010.3912546JIM BEAM BRANDS COMPANY
81_HARDERSFIELD_10058110058F Coppola Dmd Ivry Cab Svgn750mL459.9614.992024-01-05750.020.452000SOUTHERN WINE & SPIRITS NE
91_HARDERSFIELD_10058110058F Coppola Dmd Ivry Cab Svgn750mL114.9914.992024-01-06750.020.112000SOUTHERN WINE & SPIRITS NE
InventoryIdStoreBrandDescriptionSizeSalesQuantitySalesDollarsSalesPriceSalesDateVolumeClassificationExciseTaxVendorNoVendorName
4901_HARDERSFIELD_11828111828Cupcake Svgn Bl750mL759.438.492024-01-23750.020.799815WINE GROUP INC
4911_HARDERSFIELD_11828111828Cupcake Svgn Bl750mL216.988.492024-01-24750.020.229815WINE GROUP INC
4921_HARDERSFIELD_11828111828Cupcake Svgn Bl750mL216.988.492024-01-28750.020.229815WINE GROUP INC
4931_HARDERSFIELD_11828111828Cupcake Svgn Bl750mL216.988.492024-01-30750.020.229815WINE GROUP INC
4941_HARDERSFIELD_118611186Evan Williams Single Barrel750mL127.9927.992024-01-18750.010.793924HEAVEN HILL DISTILLERIES
4951_HARDERSFIELD_11888111888Penfolds Koonunga Hills Shir750mL110.9910.992024-01-01750.020.114425MARTIGNETTI COMPANIES
4961_HARDERSFIELD_11888111888Penfolds Koonunga Hills Shir750mL110.9910.992024-01-02750.020.114425MARTIGNETTI COMPANIES
4971_HARDERSFIELD_11888111888Penfolds Koonunga Hills Shir750mL110.9910.992024-01-09750.020.114425MARTIGNETTI COMPANIES
4981_HARDERSFIELD_11888111888Penfolds Koonunga Hills Shir750mL110.9910.992024-01-13750.020.114425MARTIGNETTI COMPANIES
4991_HARDERSFIELD_11888111888Penfolds Koonunga Hills Shir750mL221.9810.992024-01-15750.020.224425MARTIGNETTI COMPANIES